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  • spCLUE: a contrastive learning approach to unified spatial . . .
    Overview of the spCLUE model Taking a two-slice dataset as an example, spCLUE begins by constructing a multi-view graph (spatial view and expression view) for each slice
  • GitHub - JinmiaoChenLab GraphST
    Each cluster is regarded as a spatial domain, containing spots with similar gene expression profiles and spatially proximate GraphST can jointly analyze multiple ST samples while correcting batch effects, which is achieved by smoothing features between spatially adjacent spots across samples
  • GrapHist: Graph Self-Supervised Learning for Histopathology
    Self-supervised vision models have achieved notable success in digital pathology However, their domain-agnostic transformer architectures are not originally designed to account for fundamental biological elements of histopathology images, namely cells and their complex interactions In this work, we hypothesize that a biologically-informed modeling of tissues as cell graphs offers a more
  • Spatially informed clustering, integration, and deconvolution of . . .
    Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and discriminative spot representations from spatial transcriptomics data
  • Welcome to GraphST’s documentation! — GraphST 1. 1 documentation
    Each cluster is regarded as a spatial domain, containing spots with similar gene expression profiles and spatially proximate GraphST can jointly analyze multiple ST samples while correcting batch effects, which is achieved by smoothing features between spatially adjacent spots across samples
  • GraphST | 二十一世纪是生命科学的
    批次效应主要源于批次之间特征分布的差异。 批次效应消除：在GraphST中，有两个方面有助于消除批次效应： 首先，GraphST通过迭代地聚合邻居的表示来学习表示，这平滑了批次的特征分布，并有助于减少批次之间的差异。
  • Spatially informed clustering, integration, and deconvolution of . . .
    We demonstrated GraphST on multiple tissue types and technology platforms GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues
  • GraphST README. md at main · JinmiaoChenLab GraphST · GitHub
    Each cluster is regarded as a spatial domain, containing spots with similar gene expression profiles and spatially proximate GraphST can jointly analyze multiple ST samples while correcting batch effects, which is achieved by smoothing features between spatially adjacent spots across samples
  • spCLUE README. md at main · EnchantedJoy spCLUE · GitHub
    Taking a two-slice dataset as an example, spCLUE begins by constructing a multi-view graph (spatial view and expression view) for each slice Next, it extracts spot representations through a graph contrastive learning framework, incorporating a batch prompting module, a clustering contrastive module, and an instance contrastive module
  • spCLUE: a contrastive learning approach to unified spatial . . .
    For single-slice analysis, we compared spCLUE with nine alternative methods spCLUE was among the fastest methods overall, becoming only slightly slower than SEDR on larger datasets such as MOB1 and MOB2





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